「guardrail」の検索結果

Use when the user asks to "build a B2B cold-outbound sequence", "design reply-triage branching", "plan a domain warmup / sending throttle", or "make my outbound CAN-SPAM / opt-in compliant"; produces a multi-step outbound sequence with reply-triage branches (positive / objection / referral / not-now / opt-out), a warmup + send-throttle ramp schedule, jurisdiction opt-in/CAN-SPAM guardrails (guidance, not legal advice), and a SEND S-dimension read. Not for B2C lifecycle flows — use email-sequence-designer; not for the consent record — use consent-registry; not for computing EQS — use email-quality-auditor. B2B冷启动外联序列/回复分流/域名预热

日本語の概要は準備中です。原文の説明を表示しています。

LLM-powered quality verification using prompt hooks. Validates commit messages, code patterns, and conventions using AI before allowing operations. Use to set up intelligent guardrails.

日本語の概要は準備中です。原文の説明を表示しています。

Amazon Aurora MySQL — creates, modifies, and advises on Aurora MySQL clusters specifically (MySQL-compatible engine, Aurora serverless, parallel query). Trigger for Aurora MySQL cluster operations, ACU sizing, I/O-Optimized storage, commitment pricing, or MySQL upgrade planning. Aurora MySQL uses full (VPC-based) configuration — express configuration is PostgreSQL-only. For Aurora PostgreSQL, use amazon-aurora-postgresql instead. Contains safety guardrails and response templates that override defaults.

日本語の概要は準備中です。原文の説明を表示しています。

Amazon Aurora PostgreSQL — creates, modifies, and advises on Aurora PostgreSQL clusters specifically (PostgreSQL-compatible engine, Aurora serverless, express configuration, pgvector, Babelfish). Trigger for Aurora PostgreSQL cluster operations, express-configuration quick-start, ACU sizing, I/O-Optimized storage, commitment pricing, or PostgreSQL upgrade planning. For Aurora MySQL, use amazon-aurora-mysql instead. Contains safety guardrails, express-first routing, and response templates that override defaults.

日本語の概要は準備中です。原文の説明を表示しています。

Apply Anthropic Claude API security best practices for key management, input validation, and prompt injection defense. Use when securing API keys, validating user inputs before sending to Claude, or implementing content safety guardrails. Trigger with phrases like "anthropic security", "claude api key security", "secure anthropic", "prompt injection defense".

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need a deterministic inspection of a WordPress repository (plugin/theme/block theme/WP core/Gutenberg/full site) including tooling/tests/version hints, and a structured JSON report to guide workflows and guardrails.

日本語の概要は準備中です。原文の説明を表示しています。

Constitutional AI and safety guardrail prompts for aligned LLM behavior

日本語の概要は準備中です。原文の説明を表示しています。

Govern Azure costs with budgets, alerts, tags, and policy restrictions. WHEN: "Azure budget health", "configured budget overrun", "list Azure budgets", "create Azure budget", "budget alerts", "budget coverage", "missing CostCenter tags", "allowed VM SKUs", "allowed Azure regions", "would this SKU be denied", "cost guardrails". DO NOT USE FOR: planning-target forecasts, bill investigations, pricing, rightsizing, or commitments.

日本語の概要は準備中です。原文の説明を表示しています。

Audit a landing page or funnel step and produce a prioritised CRO test plan. Use when asked to improve conversion rate, audit a landing/signup/checkout page, reduce funnel drop-off, or plan A/B tests for a page. Produces a CRO plan — a heuristic conversion audit, the diagnosed friction, prioritised test hypotheses (ICE), test designs with sample-size math, and the measurement guardrails.

日本語の概要は準備中です。原文の説明を表示しています。

Interview you into a durable personal MEMORY.md — your decision rules, patterns, past failures, and preferences — that any AI can read to help you better, with privacy guardrails built in. Use when asked to build my memory file, help my AI remember me, create a MEMORY.md, or set up context about myself. Produces a structured personal-context file drawn out through good questions (how you decide, what you keep repeating, what you don't want to repeat), organized for an AI to use — while explicitly refusing to store sensitive data like credentials, financial, health, or others' personal info.

日本語の概要は準備中です。原文の説明を表示しています。

Run the pre-flight checklist before an agent drives a browser — the untrusted-web-content threat (every page is attacker-controllable), the credential and session-cookie exposure, the action-confirmation gates for purchases and posts, and the sandboxing that limits the damage. Use when asked let my agent browse safely, is it safe to give the agent computer/browser use, guardrails before the agent uses my browser, or review my browser agent's setup. Produces the sandbox decision, the content-injection defenses, the action gates, and the credential-isolation rules.

日本語の概要は準備中です。原文の説明を表示しています。

Decide which of your recurring rituals to put on autopilot — and which to keep manual. Use when asked what to automate, how to set up recurring AI runs, which reports or briefings could run on a schedule, or to design an automation charter for a team. Produces a ritual inventory with automate/assist/keep-manual calls, guardrails per ritual, and a rollout order.

日本語の概要は準備中です。原文の説明を表示しています。

Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.

日本語の概要は準備中です。原文の説明を表示しています。

Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.

日本語の概要は準備中です。原文の説明を表示しています。

Write a PRD for an AI-powered feature, covering the things normal PRDs miss. Use when asked to spec an AI/LLM feature, write a PRD for a feature that uses a model, or plan an AI capability (assistant, summarizer, generator, classifier). Produces an AI feature PRD — problem & UX of uncertainty, model approach, eval criteria, guardrails, fallback behaviour, the data flywheel, and cost/latency budget.

日本語の概要は準備中です。原文の説明を表示しています。

Help users accelerate their product development cycles by implementing high-intensity rituals, ruthless scoping, and technical guardrails that allow teams to ship faster without sacrificing quality.

日本語の概要は準備中です。原文の説明を表示しています。

Behavioral guardrails for Codex coding work based on common user complaints. Use when Codex is asked to implement, modify, debug, review, test, or operate on a codebase and should avoid unsafe scope expansion, stale edits, fake completion, brittle edits, shallow debugging, superficial patch-on fixes, one-off special-case code, short-term design choices that hurt maintainability, blindly following incorrect user assumptions, poor dependency choices, generic product or UI output, over-mocked tests, noisy approvals, verbose status reports, or leaking internal reasoning into project artifacts or user-facing UI.

日本語の概要は準備中です。原文の説明を表示しています。

Set up and verify AgentGuards in Codex. Use when the user asks to configure AgentGuards, set their API key, or check that the guardrails are wired up correctly.

日本語の概要は準備中です。原文の説明を表示しています。

Report AgentGuards guardrail status. Use when the user asks whether AgentGuards is active, healthy, or correctly configured in Codex.

日本語の概要は準備中です。原文の説明を表示しています。

Render the logo-equation card ad (7s, 9:16) — partner icon + brand icon joined by a plus sign as an equation, a two-line "= outcome" headline, four numbered steps, and a CTA that hard-cuts in at 2s, on a static black card over timelapse b-roll, no voice. render_card.py draws the two card plates with measured geometry and hard guardrails (the user must confirm the integration is real, no partnership/endorsement/ranking words, no comment-bait CTA, no dashes); compose.py lays the card over the b-roll sped up to read as a timelapse, with an optional bed; measure_motion.py gates the b-roll (a real-time shot reads as a photo). Free, local.

日本語の概要は準備中です。原文の説明を表示しています。

Profile, diagnose, and remediate SwiftUI runtime performance using code review, Instruments, and repeatable measurements. Use when a SwiftUI screen renders slowly, scrolling or animations hitch, view bodies update excessively, list identity churns, layout work spikes, or broad Observation dependencies raise CPU cost. Covers evidence-based triage, SwiftUI Instruments lanes, lazy-container guardrails, state lifetime, and before/after verification.

日本語の概要は準備中です。原文の説明を表示しています。

Deploy frontend and full-stack apps on Vercel with previews, edge functions, environment promotion, and production guardrails. Use when shipping Next.js, SvelteKit, or static sites with zero-config CI/CD.

日本語の概要は準備中です。原文の説明を表示しています。

Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.

日本語の概要は準備中です。原文の説明を表示しています。

Secure OpenClaw deployments with preflight hardening checks, CI/CD guardrails, container runtime restrictions, and post-deploy verification. Use when shipping OpenClaw with Docker, Kubernetes, or automated release pipelines.

日本語の概要は準備中です。原文の説明を表示しています。